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Capacity Planning
Published in Susmita Bandyopadhyay, Production and Operations Analysis, 2019
In order to simulate the randomness inherent in the practical problems, simulation generates random variates and random numbers. Various methods of random variates include inverse transform technique, convolution method, and acceptance–rejection method. Random number can be true random number, pseudo random number or quasi random number. True random numbers are truly random in nature but have the disadvantage that these random number cannot be regenerated, making them not suitable for various experimentation purpose. The pseudo random numbers are generated by executing some type of algorithms that endeavors to follow most of the properties of random numbers such as independence of the generated random numbers, uniform distribution of the numbers between 0 and 1, variety in the generated numbers etc. The pseudo random numbers can be regenerated and that’s why these methods for generating pseudo random numbers are applicable in practice. However, since the numbers are generated following some algorithm(s), thus these are not truly random. That’s why these numbers have been named as pseudo random numbers. Some of the famous traditional pseudo random number generators are linear congruential generator, multiple congruential generator, inverse congruential generator, combined linear congruential generator, lagged Fibonacci generator, midsquare method. However, a reputed algorithm called Mersenne Twister algorithm is also used in various latest software packages. Mersenne Twister algorithm is regarded as a very good and effective random number generator.
Ultrafast quantum random number generation based on quantum phase fluctuation unlimited by coherence time
Published in Gin Jose, Mário Ferreira, Advances in Optoelectronic Technology and Industry Development, 2019
Random numbers play an essential role in many fields, such as cryptography, scientific simulations, lotteries and fundamental physics tests (Ma X F et al. 2016). True Random Numbers (TRNs) can be generated from fundamental optical quantum processes, such as photon counting (Stipcevic M et al. 2007), branching path or time of arrival (Yan Q et al. 2014, Nie Y Q et al. 2014), vacuum fluctuations (Symul T et al. 2011, Gabriel C et al. 2011), amplified spontaneous emission (ASE) (Williams C R S et al. 2010, Li X et al. 2011, Li L et al. 2014) and laser phase noise (Qi B et al. 2010, Xu F et al. 2011, Nie Y Q et al. 2015, Yang J et al. 2016, Liu J et al. 2017, Raffaelli F et al. 2018, Kadhim F A et al. 2018), etc. To date, raw rate of quantum random numbers generation (QRNG) up to Gbps has been reported, only with schemes based on vacuum fluctuations, ASE and laser phase noise.
Modelling risk effect using Monte Carlo Technique
Published in Stephen O. Ogunlana, Prasanta Kumar Dey, Risk Management in Engineering and Construction, 2019
Random Numbers can be generated using a computer source of pseudo-random numbers. Many off-shelf applications (such as MS Excel) have built-in functions to generate RNs. Pseudo-random numbers generation is an algorithm to produce a fixed and deterministic sequence numbers that can at best be called ‘Pseudo-Random’ which behaves, according to statistical tests, like a truly random sequence. Pseudo-Random numbers are uniformly distributed within the unit interval (0,1) with equal likelihood. Uniformly distribution random number provides a basis for generating the random varieties required in a wide variety of realistic simulation problems. It is not correct to use the same random number to sample all distributions on a specific pass. The reason for this is that using the same random number would automatically imply fixed values for all variables (all values will be near their upper or lower limits).
A 0.7 pJ/bit, 1.5 Gbps Energy-Efficient Image-Based True Random Number Generator
Published in IETE Journal of Research, 2023
Dhirendra Kumar, Lakshmi Likhitha Mankali, Prasanna Kumar Misra, Manish Goswami
In this paper, a novel design with implementation of image-based TRNG has been proposed. The performance improved significantly by realizing the design using a combination of meta-stability, ECC and MUX. The proposed design significantly outperformed the latest existing literature by achieving better performance and providing the optimum solution in terms of speed, energy efficiency etc. when compared to other state-of-the-art works. The maximum achievable speed of 1.5 Gbps with power dissipation of 1 mW and 0.7 pJ/bit energy efficiency shows significantly better results. NIST 800.22 statistical test suite and Kolmogorov–Smirnov and Chi-square test uniformity test confirmed the validity of generated random numbers. The high entropy of 0.999999999 (up to 10 digits) and almost zero autocorrelation factors added further validness of the proposed design. The proposed design is thus suitable for true random number generation.
A mathematically-based study of the random wheel-rail contact irregularity by wheel out-of-roundness
Published in Vehicle System Dynamics, 2022
Random number, also called as random sequence, is usually classified into two types – true random number and pseudo random number, according to the generation method [36]. The true random number is generated by physical methods, which is unpredictable before the generation, and has a lower generation rate and a very complicated implement process. Thus, it is limited to some specific research fields, although it meets the requirements of various randomness indexes. The pseudo random number is usually generated mathematically, and it is predictable in nature according to a given algorithm. Obviously, the pseudo random number is impossible to be a true random number; however, a random sequence with good statistical properties can be generated by choosing a fine computing method and reasonable parameters. On the other hand, the pseudo random number has advantages of the faster generation and simpler implementation, compared to the true random number. Therefore, the pseudo random number is employed in the present study.
Speeding Up Monte Carlo Computations by Parallel Processing Using a GPU for Uncertainty Evaluation in accordance with GUM Supplement 2
Published in NCSLI Measure, 2018
C. M. Tsui, Aaron Y. K. Yan, H. W. Lai
There are several public-domain tools for testing the quality of pseudo-random number generators (PRNG). Some examples are the DIEHARD, the NIST test suite and TestU01 [5]. TestU01 is a library of C programs containing many statistical tests, such as global uniformity test, clustering test, and run and gap tests, to name a few, for gauging the performances of PRNGs. TestU01 also provides predefined tests suites (called batteries) that may be invoked by a single function call. The SmallCrush, Crush and BigCrush batteries are commonly used suites. The SmallCrush battery contains the smallest number of tests and may be completed in less than one minute. The Crush battery contains more tests and takes around one hour to complete. The BigCrush battery is the most stringent test suite and takes several hours to run on a 2.4 GHz AMD Athlon 64 CPU. A PRNG that passes the BigCrush battery is generally considered good enough for MCM applications.